NDLI: Fuzzy-sliding model reference learning control of inverted pendulum with big bang

Content Provider	IEEE Xplore Digital Library
Author	Aliasghary, M. Eksin, I. Guzelkaya, M.
Copyright Year	2011
Description	Author affiliation: Control Engineering Department, Faculty of Electronics Engineering, Istanbul Technical University, Istanbul, Turkey (Aliasghary, M.; Eksin, I.; Guzelkaya, M.)
Abstract	In this paper, a fuzzy-sliding model reference learning controller is proposed in which optimal scaling factors are assigned for the fuzzy sliding mode controllers. As the name of this study suggest the method is a breeding or hybrid combination of the fuzzy-sliding mode control (FSMC) and fuzzy model reference learning control (FMRLC) which inherits the benefits of these two methods. The main advantage of the proposed controller is that the number of rules has been reduced dramatically in comparison with the traditional FMRLC since fuzzy-sliding mode controllers are invoked in place of standard fuzzy logic controllers. The input and output scaling factors of fuzzy sliding mode controllers are adjusted using big bang - big crunch optimization method to provide an optimal result. The simulations for the proposed method are done on the inverted pendulum system. The results of these simulations demonstrate that the FS-MRLC achieves a robust performance with minimum number of fuzzy rules.
Starting Page	380
Ending Page	384
File Size	289369
Page Count	5
File Format	PDF
ISBN	9781457716768
ISSN	21647151
e-ISBN	9781457716768
DOI	10.1109/ISDA.2011.6121685
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-22
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy logic Adaptation models Fuzzy sets Sliding Mode Control Simulation Adaptive Control Fuzzy Control Robustness Mathematical model Big Bang-Big Crunch Fuzzy Model Reference Learning Sliding mode control
Content Type	Text
Resource Type	Article

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